Single-Image Camera Parameter Estimation for Realistic 3D Insertion
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Solution Overview
Problem
Existing computer vision models lack depth of field and blur estimation, leading to unrealistic insertion of objects in images, as they do not account for the camera and lens parameters used in capturing the photo.
Innovation Solution
A set of neural networks, including a defocus network and a disparity network, are used to generate pixel-wise depth and disparity estimates, which are then used to estimate camera lens parameters for virtual rendering, allowing accurate insertion and movement of objects with realistic depth and blur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision models generate per-pixel blur estimation, then blur information is provided, but the models still cannot accurately represent the physical camera parameters needed for realistic object insertion
Solution Approach 1:
The patent transforms per-pixel blur estimates into parametric lens parameters (focus distance, aperture, focal length) that characterize the entire optical system. This parameter transformation enables the blur information to be used for virtual object insertion by matching the physical camera parameters, resolving the contradiction between blur estimation accuracy and virtual object insertion capability
Solution Approach 2:
The patent introduces an intermediate processing step that converts per-pixel blur estimates into parametric lens representations. This intermediary transformation layer bridges the gap between pixel-level blur information and camera-level parameters, enabling both accurate blur representation and realistic virtual object integration
2Ease of manufacture
If images are generated without depth of field and blur estimation, then the generation process is simpler, but the inserted objects appear unrealistic and out of focus
Solution Approach 1:
The patent performs preliminary estimation of camera lens parameters from the input image before object insertion. By pre-determining the focus distance, aperture, and focal length parameters, the system prepares the optical characteristics in advance, ensuring that inserted objects can be realistically rendered with correct depth of field and blur without complicating the overall generation process
3Adaptability or versatility
If parametric lens parameters are estimated from a single image, then object insertion with correct depth and blur is enabled, but the computational complexity increases due to multiple neural networks
Solution Approach 1:
The patent merges multiple neural networks (defocus network, disparity network) into a unified architecture that jointly estimates parametric lens parameters from a single image. By combining these networks and sharing features, the system achieves accurate parameter estimation while reducing overall computational complexity compared to using separate independent networks
Data Source
AI summary
In various examples, a set of camera parameters associated with an input image are determined based on a disparity map and a signed defocus map. For example, a disparity model generates the disparity map indicating disparity values associated with pixels of the input image and a defocus model generates a signed defocus map indicating blur values associated with the pixels of the input image.